#!/usr/bin/env python
# coding=utf-8
'''
Author: John
Email: johnjim0816@gmail.com
Date: 2021-04-13 11:00:13
LastEditor: John
LastEditTime: 2021-04-15 01:25:14
Discription: 
Environment: 
'''
import numpy as np
import torch


class ReplayBuffer(object):
	def __init__(self, n_states, n_actions, max_size=int(1e6)):
		self.max_size = max_size
		self.ptr = 0
		self.size = 0
		self.state = np.zeros((max_size, n_states))
		self.action = np.zeros((max_size, n_actions))
		self.next_state = np.zeros((max_size, n_states))
		self.reward = np.zeros((max_size, 1))
		self.not_done = np.zeros((max_size, 1))
		self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
	
	def push(self, state, action, next_state, reward, done):
		self.state[self.ptr] = state
		self.action[self.ptr] = action
		self.next_state[self.ptr] = next_state
		self.reward[self.ptr] = reward
		self.not_done[self.ptr] = 1. - done
		self.ptr = (self.ptr + 1) % self.max_size  # 牛逼 超过最大值就重新算
		self.size = min(self.size + 1, self.max_size)

	def sample(self, batch_size):
		ind = np.random.randint(0, self.size, size=batch_size)
		return (
			torch.FloatTensor(self.state[ind]).to(self.device),
			torch.FloatTensor(self.action[ind]).to(self.device),
			torch.FloatTensor(self.next_state[ind]).to(self.device),
			torch.FloatTensor(self.reward[ind]).to(self.device),
			torch.FloatTensor(self.not_done[ind]).to(self.device)
		)